Abstract : In this paper, we propose to perform clustering and temporal prediction on network-level traffic states of large-scale traffic networks. Rather than analyzing dynamics of traffic states on individual links, we study overall spatial configurations of traffic states in the whole network and temporal dynamics of global traffic states. With our analysis, we can not only find out typical spatial patterns of global traffic states in daily traffic scenes, but also acquire long-term general predictions of the spatial patterns, which could be used as prior knowledge for modeling temporal behaviors of traffic flows. For this purpose, we use a locality preservation constraints based non-negative matrix factorization (LPNMF) to obtain a low-dimensional representation of network-level traffic states. Clustering and temporal prediction are then performed on the proposed compact representation. Experiments on realistic simulated traffic data are provided to check and illustrate the validity of our proposed approach.